Methods for predicting drug sensitivity in patients afflicted with an inflammatory disease

ABSTRACT

Methods are disclosed for predicting the efficacy of a drug for treating an inflammatory disease in a human patient, including: obtaining a sample of cells from the patient; obtaining a gene expression profile of the sample in the absence and presence of in vitro modulation of the cells with specific cytokines and/or mediators; and comparing the gene expression profile of the sample with a reference gene expression profile, wherein similarities between the sample expression profile and the reference expression profile predicts the efficacy of the drug for treating the inflammatory disease in the patient.

RELATED APPLICATION

This application is a continuation-in-part of U.S. application Ser. No. 09/947,991, filed Sep. 6, 2001. The entire teachings of the above application are incorporated herein by reference.

BACKGROUND OF THE INVENTION

The field of pharmacogenomics measures differences in the effect of medications that are caused by genetic variations. Such differences are manifested by differences in the therapeutic effects or adverse events of drugs. For most drugs, the genetic variations that potentially characterize drug-responsive patients from non-responders remain unknown.

SUMMARY OF THE INVENTION

The present invention relates to methods for determining a patient's responsiveness to treatment for asthma and related inflammatory conditions.

In one embodiment, the invention is directed to a method for predicting the efficacy of a drug for treating an inflammatory disease in a human patient, comprising: obtaining a sample of cells from the patient; obtaining a gene expression profile of the sample in the absence and presence of in vitro modulation of the cells with specific mediators; and comparing the gene expression profile of the sample with a reference gene expression profile, such that similarities between the sample expression profile and the reference expression profile predicts the efficacy of the drug for treating the inflammatory disease in the patient. The reference gene expression profile can comprise expression levels of one or more informative genes listed in Tables 1, 2A, 2B, 4A, 4B and 5A–5E. The similarity between the sample expression profile and the reference expression profile predicts the efficacy of the drug for treating the inflammatory disease in the patient. In a particular embodiment, the sample is exposed to the drug for treating the inflammatory disease prior to obtaining the gene expression profile of the sample. In a particular embodiment, the sample is exposed to the drug for treating the inflammatory disease prior to obtaining the gene expression profile of the sample. The inflammatory disease can be asthma, atopy, rheumatoid arthritis, juvenile chronic arthritis, psoriasis, inflammatory bowel disease (IBD) and sepsis. The atopic inflammatory disease can be rhinitis, conjunctivitis, dermatitis and eczema. Drugs can be corticosteroids, β2-agonists and leukotriene antagonists for asthma. In addition, for inflammatory diseases, the drug can be a symptom reliever or anti-inflammatory drug for an inflammatory disease condition. In one embodiment, the sample of cells can be derived from peripheral blood mononuclear cells or neutrophils. In a particular embodiment, the gene expression profile of the sample can be obtained using a hybridization assay to oligonucleotides contained in, for example, a microarray. In another embodiment, the expression profile of the sample can be obtained by detecting the protein product of informative genes. The detection of protein products can include the use of, for example, antibodies capable of specifically binding protein products of the informative genes. The reference expression profile can be, for example, that of cells derived from healthy, non-atopic, non-asthmatic individuals. In another embodiment, the reference expression profile can be that of cells derived from patients that do not have an inflammatory disease. In one embodiment, the cells are treated with the drug candidate before the expression profile is obtained.

In yet another embodiment, the invention is directed to a method of screening for glucocorticoid sensitivity in an asthmatic patient including: obtaining a sample of cells from the patient; obtaining a gene expression profile from the sample in the absence and presence of in vitro activation of the cells with specific cytokines (mediators); and comparing the gene expression profile of the sample with a reference gene expression profile, wherein similarity in expression profiles between the sample and reference profiles indicates glucocorticoid sensitivity in the patient from whom the sample was obtained.

In yet another embodiment, the invention is directed to a method for predicting efficacy in a human asthmatic patient of leukotriene antagonists, including: obtaining a sample of cells from the patient; obtaining a gene expression profile from the sample in the absence and presence of in vitro modulation of the cells with specific mediators; and comparing the gene expression profile of the sample with a reference gene expression profile, wherein similarity in expression profiles between the sample and reference profiles predicts the efficacy in the human asthmatic patient of leukotriene antagonists.

In another embodiment, the invention is directed to an expression profile comprising expression levels of gene products from one or more genes described in Tables 1, 2A, 2B, 4A, 4B and 5A–5E.

In another embodiment, the invention is directed to a method for predicting the efficacy in a human asthma patient of leukotriene antagonists including, but not limited to, montelukast (a.k.a., SINGULAIR™; Merck, Whitehouse Station, N.J.), zafirlukast (a.k.a., ACCOLATE™, AstraZeneca, Wilmington, Del.), and zileuton (a.k.a., ZYFLO™; Abbott Laboratories, Chicago, Ill.), comprising: obtaining a sample of cells from the patient; obtaining a gene expression profile from the sample in the absence and presence of in vitro modulation of the cells with specific mediators; and comparing the gene expression profile of the sample with a reference gene expression profile, wherein similarity in expression profiles between the sample and reference profiles predicts the efficacy in the human asthmatic patient of leukotriene antagonists.

In another embodiment, the invention is directed to a kit for predicting the efficacy of a drug for treating an inflammatory disease in a human patient according to the methods described herein comprising hybridization probes capable of hybridizing to polynucleotides corresponding to pre-determined informative genes and reagents for detecting hybridization. In a different embodiment, the invention is directed to a kit for predicting the efficacy of a drug for treating an inflammatory disease in a human patient according to the methods described herein comprising antibodies capable of specifically binding protein products of pre-selected informative genes and reagents for detecting antibody binding.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention, as illustrated in the accompanying drawings.

FIG. 1 is a representative Bayesian matrix plot demonstrating the predictive accuracy of a naïve Bayesian classifier in predicting drug response phenotypes of 14 glucocorticoid resistant (GC-R) and 14 glucocorticoid sensitive (GC-S) patients. The 50 genes predicted the correct drug response phenotype with 82% accuracy.

FIG. 2 is a representative Bayesian matrix plot demonstrating predictive accuracy of a naïve Bayesian classifier in predicting drug response phenotype of GC-R and GC-S patients when all 54 patients were included in training the classifier. Fifty genes were selected that predicted the correct drug response phenotype with 81% accuracy.

FIG. 3 is a representative Bayesian matrix plot demonstrating the predictive accuracy of a naïve Bayesian classifier in predicting a drug response phenotype of an independent cohort of 14 GC-R patients that were not used in the process of gene selection or in training of the classifier. The 50 genes selected predicted the correct drug response phenotype with 86% accuracy.

FIG. 4 is a representative matrix plot showing the differential expression of 15 genes (see Table 5) that were examined in 88 patients. In addition to the genes listed above, all of which carry a predicted weight, these 15 genes most accurately differentiated a cohort of GC responders from non-responders using the induction time point for the training set. Together, these genes predicted the correct GC response phenotype of an independent patient cohort of equal size with 86% accuracy.

FIG. 5 is a plot demonstrating predictive accuracy of a naïve Bayesian classifier in predicting the drug response phenotype of a cohort of 12 Leukotriene Sensitive (LT-S) and 12 Leukotriene Resistant (LT-R) patients using the “leave one out cross validation” (LOOCV) method. The 12 genes selected predicted the correct drug response phenotype with over 90% accuracy.

DETAILED DESCRIPTION OF THE INVENTION

A description of preferred embodiments of the invention follows.

The present invention is directed to methods for predicting efficacy of drug treatment in asthma patients and to methods for screening drug candidates useful in treating inflammatory diseases. Current methods of treating asthma involve the use of corticosteroids, β2-agonists or leukotriene antagonists. Although asthma has been treated by these methods for several years, a significant fraction of asthma patients are resistant to treatment. As there are risks associated with methods for treating asthma, identification of patients that will be responsive to treatment is important. Methods described herein are used to identify genes that are differentially expressed in responsive patients when compared to non-responsive patients, thereby allowing for a convenient determination of patients that are responsive to treatment. Described herein are methods for activating in cultured cells obtained from patient samples and methods for utilizing said cultured cells for drug screening and obtaining expression profiles.

Asthma, or Reversible Obstructive Airway Disease (ROAD), is a condition in which the airways of the lungs become either narrowed or completely blocked, impeding normal breathing and leading to potentially more severe health problems. Although normal airways have the potential for constricting in response to allergens or irritants, the asthmatic's airways are oversensitive or hyper-reactive. In response to stimuli, the airways may become obstructed by one of the following: constriction of the muscles surrounding the airway; inflammation and swelling of the airway; or increased mucus production that clogs the airway. Once the airways have become obstructed, it takes more effort to force air through them, so that breathing becomes labored. Because exhaling through the obstructed airways is difficult, too much stale air remains in the lungs after each breath. This accumulation of stale air decreases the amount of fresh air that can be taken in with each new breath, so not only is there less oxygen available for the whole body, the high concentration of carbon dioxide in the lungs causes the blood supply to become acidic as well. This acidity in the blood may rise to toxic levels if the asthma remains untreated.

Although asthma creates difficulties in breathing and can lead to more serious problems, the lung obstruction associated with asthma is reversible, either spontaneously or with medication. Asthmatics can take anti-inflammatory agents such as corticosteroids, bronchodilators and leukotriene antagonists to reduce inflammation and asthma symptoms.

Corticosteroids are sometimes also referred to as “steroids.” This type of medication is not related to the anabolic steroids that are misused by some athletes to increase performance. Rather, corticosteroids have been used as a treatment for asthma and allergies since 1948. They decrease airway inflammation and swelling in the bronchial tubes; reduce mucus production by the cells lining the bronchial tubes; decrease the chain of overreaction (hyper-reactivity) in the airways; and help the airway smooth muscle respond to other medications. Corticosteroids can be administered in a variety of ways, such as through the use of an inhaler, topically, orally, or through injection. Topical preparations (on specific surface areas such as skin or the lining of the bronchial tubes) may be applied as creams or sprays (inhalers). Corticosteroid inhalers are recommended for patients with daily, moderate or severe asthma symptoms. Oral corticosteroids and injected corticosteroids are generally only prescribed for those with severe asthma symptoms.

Although the use of corticosteroids has been commonplace for several years, they are not always effective and significant side effects do occur. Some people experience minor side effects of hoarseness and thrush (a fungal infection of the mouth and throat) from using corticosteroid inhalers. Also, long-term use of inhaled corticosteroids has been implicated in reduced growth velocity in children. Oral corticosteroids can have more side effects than inhaled corticosteroids. Oral corticosteroids are prescribed for long durations only when other treatments have failed to restore normal lung function and the risks of uncontrolled asthma are greater than the side effects of the steroids. For example, prednisone, one of the most commonly prescribed corticosteroids, can lead to possible side effects of weight gain, increased appetite, menstrual irregularities and cramps, heartburn, and indigestion. Some patients experience side effects such as loss of energy, poor appetite, and severe muscle aches or joint pains when their dosage of cortisone tablets is decreased. Long-term oral corticosteroid use may cause side effects such as ulcers, weight gain, cataracts, weakened bones and skin, high blood pressure, elevated blood sugar, easy bruising and decreased growth in children. Such side effects indicate a need to accurately assess the efficacy of corticosteroid treatment in asthmatic patients.

Bronchodilators, also called “β2-agonists”, are non-steroidal anti-inflammatory medications often used as short-term “rescue” medications to immediately relieve asthma symptoms. Bronchodilators include albuterol, bitolterol, pirbuterol and terbutaline. Additionally, salmeterol is a long-acting β2-agonist that is intended to be used on a long-term basis, along with an anti-inflammatory medication, for controlling asthma. Those using salmeterol should take the medication on a daily basis, even if they are feeling fine, as it prevents symptoms. Although sporadically effective, bronchodilators are not typically useful in cases of severe asthma.

Many of the cells involved in causing airway inflammation are known to produce signaling molecules within the body called “leukotrienes.” Leukotrienes are responsible for causing the contraction of the airway smooth muscle, increasing leakage of fluid from blood vessels in the lung, and further promoting inflammation by attracting other inflammatory cells into the airways. Oral anti-leukotriene medications have been introduced to fight the inflammatory response typical of allergic disease. These drugs are used in the treatment of chronic asthma. Recent data demonstrates that prescribed anti-leukotriene medications can be beneficial for many patients with asthma, however, a significant number of patients do not respond to anti-leukotriene drugs.

The present invention relates to methods for determining the treatment outcome of drugs used to treat inflammatory conditions such as asthma. The methods rely on the identification of genes that are differentially expressed in samples obtained from patients and are associated with clinical responsiveness to the drug under study. The particular genes, herein referred to as “informative genes,” are identified in cells that have been induced to mimic the disease condition (e.g., asthma), or in tissue samples from patients diagnosed with asthma or other inflammatory diseases. Informative genes can be identified, for example, by determining the ratio of gene expression in induced versus uninduced cells and comparing the results between patients with variable drug sensitivity. Alternatively, informative genes can be identified based on the ratio of gene expression in disease versus normal tissue samples, or, in the case of informative genes used to identify drug responsiveness, informative genes can be identified by the ratio of gene expression in cells exposed to the drug versus cells not exposed to the drug, in subjects who qualify as responders versus non-responders to the drug. A ratio of 1.0 would indicate the gene is expressed at the same level in both samples. Ratios greater than one indicate increased expression over normal or uninduced cells, whereas ratios less than one indicate reduced expression relative to normal or uninduced cells.

A subset or all informative genes can be assayed for gene expression in order to generate an “expression profile” for responsive versus non-responsive patients. As used herein, an “expression profile” refers to the level or amount of gene expression of one or more informative genes in a given sample of cells at one or more time points. A “reference” expression profile is a profile of a particular set of informative genes under particular conditions such that the expression profile is characteristic of a particular condition. For example, a reference expression profile that quantitatively describes the expression of the informative genes listed in Tables 1, 2A, 2B, 4A, 4B and 5A–5E can be used as a reference expression profile for drug treatment responsiveness. In one embodiment, expression profiles are comprised of the fifty informative genes that exhibit differential expression, and provide sufficient power to predict the responsiveness to the drug with high accuracy. Other embodiments can include, for example, expression profiles containing about 5 informative genes, about 25 informative genes, about 100 informative genes, or any number of genes in the range of about 5 to about 400 informative genes. The informative genes that are used in expression profiles can be genes that exhibit increased expression over normal cells or decreased expression versus normal cells. The particular set of informative genes used to create an expression profile can be, for example, the genes that exhibit the greatest degree of differential expression, or they can be any set of genes that exhibit some degree of differential expression and provide sufficient power to accurately predict the responsiveness to the drug. The genes selected are those that have been determined to be differentially expressed in either a disease, drug-responsiveness, or drug-sensitive cell relative to a normal cell and confer power to predict the response to the drug. By comparing tissue samples from patients with these reference expression profiles, the patient's susceptibility to a particular disease, drug-responsiveness, or drug-resistance can be determined.

The generation of an expression profile requires both a method for quantitating the expression from informative genes and a determination of the informative genes to be screened. The present invention describes screening specific changes in individuals that affect the expression levels of gene products in cells. As used herein, “gene products” are transcription or translation products that are derived from a specific gene locus. The “gene locus” includes coding sequences as well as regulatory, flanking and intron sequences. Expression profiles are descriptive of the level of gene products that result from informative genes present in cells. Methods are currently available to one of skill in the art to quickly determine the expression level of several gene products from a sample of cells. For example, short oligonucleotides complementary to mRNA products of several thousand genes can be chemically attached to a solid support, e.g., a “gene chip,” to create a “microarray.” Specific examples of gene chips include Hu95GeneFL (Affymetrix, Santa Clara, Calif.) and the 6800 human DNA gene chip (Affymetrix, Santa Clara, Calif.). Such microarrays can be used to determine the relative amount of mRNA molecules that can hybridize to the microarrays (Affymetrix, Santa Clara, Calif.). This hybridization assay allows for a rapid determination of gene expression in a cell sample. Alternatively, methods are known to one of skill in the art for a variety of immunoassays to detect protein gene expression products. Such methods can rely, for example, on conjugated antibodies specific for gene products of particular informative genes.

Informative genes can be identified, for example, in samples obtained from individuals identified through database screening to have a particular trait, e.g., glucocorticoid sensitivity (GC-S) or glucocorticoid resistance (GC-R). In addition, informative genes identified in cultured cells can be verified by obtaining expression profiles from samples of known asthma patients that are either responsive or non-responsive to a particular drug treatment. An example of a combination of obtaining samples from patients and searching particular databases for the genealogical and medical history of the individual from whom the sample was obtained is herein described for the genetically isolated population of Iceland.

The population of Iceland offers a unique opportunity to identify genetic elements associated with particular disorders. The unique opportunity is available due to at least three conditions: 1) the Icelandic population is genetically isolated; 2) detailed genealogical records are available; and 3) detailed medical records have been kept dating back to 1915. The identification of differentially expressed genes in responsive versus non-responsive patients would occur after an examination of a patient's genealogical past as well as the medical records of close relatives in addition to data obtained from samples derived from the individual.

An examination of genealogical and medical records identifies modern day individuals with a family history of exhibiting a particular trait. For example, individuals can be found that are asthmatic and that respond to a particular asthma drug treatment, and an examination of a genealogical database might confirm that indeed the individual's close relatives exhibit the same traits, on average, more than the rest of the population. Thus, a tentative conclusion can be drawn that the individual in question likely has genetic determinants that could be used to identify responsive and non-responsive patients. Samples obtained from this individual, combined with samples obtained from other such individuals, are genotyped by any of the methods described above in order to identify informative genes that can subsequently be used to generate reference expression profiles.

Informative genes can also be identified ex vivo in cells derived from patient samples. For example, a tissue sample can be obtained from a patient and cells derived from this sample can be cultured in vitro. The cells can be cultured in the presence or absence of cytokines, e.g., tumor necrosis factor alpha (hereinafter, “TNFα”) and interleukin 1-beta (hereinafter, “IL-1β”), or other mediators such as, for example, leukotriene receptor agonists, e.g., LTD₄. As used herein, “mediator” refers to a molecular signal for a particular event. Cytokines are an example of a class of mediators that are low molecular weight, pharmacologically active proteins that are secreted by one cell for the purpose of altering either its own functions (autocrine effect) or those of adjacent cells (paracrine effect). In some instances, cytokines enter the circulation and have one or more of their effects systemically. Expression profiles of informative genes can be obtained from sample-derived cells in the presence and/or absence of cytokines or other mediators, and these profiles can be compared to reference expression profiles to determine sensitivity or resistance to drug treatment. Additionally, cells can be cultured in the presence or absence of the drug itself prior to obtaining the expression profile.

Once informative genes have been identified, polymorphic variants of informative genes can be determined and used in methods for detecting disorders in patient samples based on which polymorphic variant is present in the sample (e.g., through hybridization assays or immune detection assays using antibodies specific for gene products of particular polymorphic variants).

Alternatively, the approach described above can be used to verify the utility of informative genes identified in cultured cells. Once identified, informative genes could be verified as to their predictive ability in more genetically diverse populations, thus ensuring the utility of the predictive power of these informative genes in populations in addition to the genetically isolated population of, e.g., Iceland.

The “genetic isolation” of the Icelandic population implies a low degree of allelic variation among individuals. This circumstance reduces the background in screening for differences in a population. In “genetically diverse” populations, many differences appear between individuals that might contribute to the same trait. For example, an examination of individuals responsive for asthma drug treatment might produce a finite yet large number of genetic differences with respect to non-responsive individuals. However, in a genetically diverse population, a great majority of these genetic differences are “artifactual” or background “noise signals” detected because of the diversity of the population. For a genetically isolated population, fewer differences would be expected to be found between the two groups, providing a higher probability that the differences that are discovered are likely to be directly related to the trait in question, in this case, responsiveness to asthma drug treatment. Once determined in a genetically isolated environment, the utility of informative genes and expression profiles based on those informative genes can be verified for more general use in a genetically diverse population.

As elevated levels of both TNFα and IL-1β are characteristic of asthma and other inflammatory diseases (including, but not limited to, atopy (e.g., rhinitis, conjunctivitis, dermatitis, eczema), rheumatoid arthritis, juvenile chronic arthritis, psoriasis, IBD and sepsis), cells exhibiting elevated cellular levels of these cytokines can be used to determine drug efficacy for related inflammatory diseases. As used herein, “efficacy” describes a range of effectiveness from non-effective (non-responsive) to completely effective, and degrees between the two extremes. The present invention is directed in part to comparing gene expression profiles of activated peripheral blood mononuclear (PBM) cells or neutrophils isolated from patients with asthma or related inflammatory conditions to gene expression profiles of activated control (non-asthmatic) PBM cells or neutrophils. As used herein, “activated” refers to treating cells with cytokines or other mediators of asthma or related inflammatory diseases. Such activation can be achieved by elevating levels of cytokines such as TNFα and IL-1β. Activated cells derived from patient samples can be used to screen for drug candidates as well as provide for sample and reference expression profiles useful in diagnosing asthma and other inflammatory diseases.

The cellular levels of TNFα and IL-1β can be increased by a variety of methods known in the art. For example, mammalian cells, such as PBM cells, neutrophils, synovial cells or airway smooth muscle (ASM) cells, grown in culture can be exposed to isolated and purified TNFα and IL-1β such that these cytokines are taken up by the cells (typically, exposure of about 4 hours of TNFα at a concentration of 5 ng/mL and IL-1β at a concentration of 1 ng/mL in culture will produce pro-asthma like symptoms in cultured cells). Other methods for expression of cytokines in cells grown in culture, e.g., by transfection of genes cloned into expression vectors, are known in the art.

TNF-related pathologies or diseases, as would be mimicked by the pro-inflammatory like conditions induced in the cells described herein, include, but are not limited to, inflammatory diseases or disorders, infections, neurodegenerative diseases, malignant pathologies, cachectic syndromes and certain forms of hepatitis.

Inflammatory diseases or disorders, include, but are not limited to, acute and chronic immune and autoimmune pathologies, such as, but not limited to, rheumatoid arthritis (RA), juvenile chronic arthritis (JCA), psoriasis, graft versus host disease (GVHD), scleroderma, diabetes mellitus, allergy; asthma, acute or chronic immune disease associated with an allogenic transplantation, such as, but not limited to, renal transplantation, cardiac transplantation, bone marrow transplantation, liver transplantation, pancreatic transplantation, small intestine transplantation, lung transplantation and skin transplantation; chronic inflammatory pathologies such as, but not limited to, sarcoidosis, chronic inflammatory bowel disease, ulcerative colitis, and Crohn's pathology or disease; vascular inflammatory pathologies, such as, but not limited to, disseminated intravascular coagulation, atherosclerosis, Kawasaki's pathology and vasculitis syndromes, such as, but not limited to, polyarteritis nodosa, Wegener's granulomatosis, Henoch-Schönlein purpura, giant cell arthritis and microscopic vasculitis of the kidneys; chronic active hepatitis; Sjögren's syndrome; psoriatic arthritis; enteropathic arthritis; reactive arthritis and arthritis associated with inflammatory bowel disease; and uveitis.

Infections include, but are not limited to, sepsis syndrome, cachexia (e.g., TNFα-mediated effects), circulatory collapse and shock resulting from acute or chronic bacterial infection, acute and chronic parasitic and/or infectious diseases, bacterial, viral or fungal, such as a human immunodeficiency virus (HIV), acquired immunodeficiency syndrome (AIDS) (including symptoms of cachexia, autoimmune disorders, AIDS dementia complex and infections).

Neurodegenerative diseases include, but are not limited to, demyelinating diseases, such as multiple sclerosis and acute transverse myelitis.

Malignant pathologies are associated with TNFα-secreting tumors or other malignancies involving TNFα, such as, for example, leukemias (acute, chronic myelocytic, chronic lymphocytic and/or myelodyspastic syndrome) and lymphomas (Hodgkin's and non-Hodgkin's lymphomas, such as malignant lymphomas (Burkitt's lymphoma or Mycosis fungoides)).

Cachectic syndromes and other pathologies and diseases involving excess TNFα, include, but not limited to, cachexia of cancer, parasitic disease and heart failure.

Elevated levels of TNFα are also associated with certain types of hepatitis, including, but not limited to, alcohol-induced hepatitis and other forms of chronic hepatitis.

One of skill in the art will recognize that reagents necessary to utilize the methods described herein can be contained in a kit. Such reagents as described are either commercially available (e.g., buffered solutions, chemical reagents) or produced by methods known in the art (e.g., oligonucleotides, antibodies, ligands for detection). Thus, one of skill in the art would recognize that a kit can be produced containing in appropriate compartments, for example, all reagents, probes, and materials necessary for to allow for the practice of the methods described herein.

The invention will be further described with reference to the following non-limiting examples. The teachings of all the patents, patent applications and all other publications and websites cited herein are incorporated by reference in their entirety.

EXEMPLIFICATION Example 1

Predictive Value of Expression Profiles in Human Patients.

Asthma is a common complex disease with a variable phenotype. While the cellular and molecular mechanisms that underlie asthma remain largely unknown, elevated levels of the pleiotropic cytokines, IL-1β and TNFα, have been implicated in the pathophysiology of asthma as well as in various other inflammatory disorders (Broide, D. et al., 1992, J. Allergy Clin. Immunol. 89:958–967; Arend, W., 2001, Arthritis Rheum. 45:101–106).

It has been widely accepted that diseases such as asthma that are common in the general population and have been demonstrated to have a strong, but complex, genetic component together with variable responsiveness to drugs, present ideal candidate disease targets for pharmacogenetic research. The latter holds great promise in optimization of individual specific therapy as well as providing new targets for drug development. Improved, preferably prophylactic, treatment of asthma patients is desired because the drugs now used are not effective in all patients, allow recurrence of the symptoms in a high percentage of patients, and sometimes have severe adverse side effects. The ability to analyze the expression level of thousands of genes in a single assay, using DNA microarrays, allows for a powerful screen of multiple molecular gene pathways, simultaneously, that may elucidate differential expression in genes encoding enzymes, kinases, ion channels, and other signaling molecules that determine individual's variation in response to drugs.

Accordingly, using high-density DNA microarray analysis, differences in mRNA expression of PBM cells freshly isolated from GC-S and GC-R asthmatics were identified. The mRNAs were examined at baseline (T0) and to the combined effects of IL-1β and TNFα. Moreover, in an attempt to further elucidate those genes that may contribute to responsiveness of GC, we examined the effects of GC treatment on altered gene expression in cells that were activated by IL-1β and TNFα. The rationale for using this strategy was based on two well-established concepts. First, the symptoms of asthma are mechanistically channeled through the actions of IL-1β and TNFα. Second, glucocorticoids act in asthmatics by altering the expression of genes that are modulated by pro-inflammatory cytokines. The results provide new evidence demonstrating: 1) of 12,600 genes examined, 50 genes selected by algorithms based on the naïve Bayesian classifier predicted the correct GC-R phenotype of the 14 GC-R and 14 GC-S patients that it was trained on with 82% accuracy; 2) when a second cohort of 26 GC-R asthmatics was tested, the predictive accuracy of the classifier was 86%, and; 3) among the genes selected there were several cell signaling molecules, transcription factors and pro-inflammatory molecules potentially associated with regulation of GC responsiveness. This is the first demonstration using gene expression profiles in freshly isolated PBM cells that differentiate between GC-R and GC-S patients that provide sufficient power to predict response to glucocorticoids in asthmatics with high accuracy.

Methods and Materials

Patients. The patient population studied was selected from the private and outpatients clinics of practicing allergists at the Allergy/Immunology Division of the National University Hospital of Iceland. A total of 1185 patient records were screened for phenotypic information and analyzed with respect to the Icelandic Genealogy Database to determine the family connections of the patients. Patients carrying a diagnosis of asthma who were using inhaled glucocorticoid medications were evaluated further. Fifty-four patients age 18–70 years were initially randomly recruited to participate and were divided into two cohorts. The first cohort consisted of 14 GC-S and 14 GC-R patients, together with 14 control subjects who had no evidence of asthma or atopy and were not using inhaled GC or any other medications. Twenty-six additional patients were also collected and used as a second cohort for the predictive classifier. The study was subsequently expanded to include 96 patients (60 GC-S and 36 GC-R), of whom, 88 completed all 3 in vitro treatment conditions (baseline, IL-1β/TNFα in the absence and presence of GC). Each cohort was subsequently randomly split into two subcohorts of equal size. A random split was performed 10 times and the most informative genes that detected GC-R from GC-S patients in the training set were used as predictors for the independent patient set of equal size. Patients were allowed to use both short and long acting β2-adrenergic drugs as well as leukotriene antagonists. Medication doses of all drugs were kept unchanged for 2 weeks and the patients had to be off oral GC for minimum of 4 weeks prior to their donation of blood for the PBM cell expression studies. A single physician, who was blinded to the expression array studies, phenotyped all patients. Upon completion of physical examination, confirmation of drug response phenotypes and informed consent authorizing his/her participation in the study, the patients were asked to donate a blood sample for the study. No tests were performed in control subjects. Forty milliliters of EDTA blood was collected and peripheral blood mononuclear (PBM) cells were isolated from the rest of the blood for the experimental studies described below.

Study Inclusion Criteria. The criteria the patients had to fulfill to enter the study, included the following:

-   -   Asthma diagnosed by an allergist/pulmonologist. The approach         used to diagnose asthma in Iceland concurs with that of the         diagnostic asthma criteria outlined by the NHLB and the American         Thoracic Society (National Institutes of Health. 1997.         Guidelines for the Diagnosis and Management of Asthma: Expert         Panel Report 2, July 1997, U.S. Government Printing Office,         Washington, D.C. NIH Publication No. 97-4051; American Thoracic         Society. Standardization of Spirometry, 1994 (update), Am. J.         Resp. Crit. Care. Med. 1995, 152:1107–1136) and include any of         the following measures:         -   Patient having recurrent symptoms of cough and wheezing for             more than 2 years and demonstrating clinical response to             bronchodilator therapy (as measured by>15% increase in             Forced Expiratory Volume in 1 second (FEV1) following             bronchodilator)         -   Patient having reduced FEV1 (FEV1<80) at baseline prior to             bronchodilator therapy and showing >15% improvement in FEV1             following bronchodilator therapy         -   Patient having recurrent symptoms of cough and wheezing and             on methacholine challenge test, performed in accordance to             ATS guidelines, there occurs >20% drop in FEV1 at             methacholine concentrations<8 mg/L     -   Methacholine challenge test were obtained in patients with         FEV1>70. In addition, skin tests to the 12 most common         aeroallergens in Iceland and total IgE levels were obtained and         history and clinical evidence of rhinitis were recorded. All         patients were re-examined by the same allergist who determined         the clinical response to GC.

Response to inhaled glucocorticoids. Patients were categorized as either glucocorticoid sensitive (GC-S) or glucocorticoid-resistant (GC-R). Any two or more combinations of the following criteria defined glucocorticoid response in GC-S patients (Barnes, P. et al. Am. J. Resp. Crit. Care. Med., 1998. 157:S1–S53):

-   -   Good control of asthma symptoms (cough and wheezing) when taking         inhaled GC in recommended therapeutic doses (up to 1000 mg of         Fluticasone; up to 800 mg of Budesonide; or up to 1000 mg of         Beclomethasone, which were the 3 inhaled GC drugs used by the         patients).     -   Improved exercise tolerance and/or fewer exacerbations following         8 or more weeks of therapeutic doses of inhaled GC.     -   Improved peak flows and/or spirometry values after 8 or more         weeks of inhaled GC.     -   Improved quality of life/well being as judged by the patient         response to a standard questionnaire, after 8 or more weeks of         inhaled GC therapy.

GC-R patients did not experience improvement in the above measures when using inhaled GC in therapeutic doses. The GC-R patients had been tried on >2,000 mg of inhaled Fluticasone (or equivalent dose of Budesonide or Beclomethasone) per day. All GC-R patients studied had either moderate or severe resistance to GC therapy. The GC-R and GC-S patients were randomly split 10 times into two cohorts each, wherein one set was used for training and the other patient set was used to generate predictors for an independent set.

Study Exclusion Criteria. The study exclusion criteria are outlined below:

-   -   Therapies, which could interfere with evaluation of efficacy or         the incidence of adverse effects, including:         -   Other investigational drugs     -   Concurrent medication (other than β2-adrenergic agonist or         anti-leukotriene drugs).     -   Diseases or conditions that could interfere with the evaluation         of efficacy or the incidence of adverse effects, including:         -   Pregnancy or lactation         -   Hypersensitivity or serious adverse experiences to asthma             drugs in the past         -   Aspirin sensitive asthma         -   Occupational asthma         -   Sensitivity to the study drug or its components         -   Compliance to medication is of question

Patient protection measures/Informed consent procedures. The 96 asthmatic patients enrolled were randomly selected from the list of 1185 asthmatic patients who fulfilled the study criteria. The response- and participation rate of the patients exceeded 95%. All patients signed an informed consent, donated blood samples, and completed a questionnaire and all tests necessary for proper phenotyping. The study was approved by the Icelandic Data Protection Commission and the National Bioethics Committee. The Data Protection Commission of Iceland subsequently encrypted personal information about the patients and their family members (Gulcher, J. and Stefansson, K. Clin. Chem. Lab. Med. 1998, 36:523–527). All blood and DNA samples were also coded for protection of patient's privacy.

Assessment of gene microarray expression. Ninety-six asthma patients were recruited in accordance with the study inclusion criteria. GC responsiveness was measured by scoring functions taking into account both clinical and laboratory parameters as described above. PBM cells (PBMCs) were isolated by the standardized Ficoll method. PBMCs were counted, and stained with FITC-conjugated anti-CD3 monoclonal antibodies (mAb), PE-conjugated anti-CD19 mAb, and FITC-conjugated anti-CD14 mAb and examined by flow cytometry to determine the relative contributions of each cell type. The cells were then divided into 3 treatment conditions (baseline, IL-1β/TNFα treatment and IL-1β/TNFα in the presence of GC treatment) with approximately 6 million cells per condition. Thereafter, multiple gene mRNA expression was examined in isolated PBMCs with gene microarray technology, using the human Hu95-A gene chip containing 12,600 DNA oligonucleotides (Affymetrix, Santa Clara, Calif.). In brief, cells were exposed for 4 hr to IL-1β (1 ng/mL) and TNFα (5 ng/mL) combined, or to media alone in the absence and presence of 1 hr pre-treatment with DEX (10⁻⁶ M), and maintained at 37° C. in a humidified atmosphere of 5% CO₂/95% air in RMPI-1640 media. Following incubation of cells, total RNA used for the microarray expression analysis was extracted and purified using commercially available reagents recommended by the manufacturer. Total RNA was extracted using Trizol and purified with Qiagen RNAEASY spin columns (Qiagen GmbH₂, Germany). Approximately 5 μg of RNA were used for first and second strand cDNA synthesis. After precipitation, cDNAs were transcribed to cRNAs by methods known in the art. The biotinylated cRNA was subsequently hybridized to the Affymetrix gene chips overnight according to the manufacturer (Affymetrix, Santa Clara, Calif.). Non-bound probes were removed by high-stringency washing. The hybridized chips were developed using a Streptavidin-PE complex and scanned. The scanned images were then analysed with Affymetrix software and the data was examined using commercially available software programs (Asher B. J. Mol. Graph Model. 2000, 18:79–82). AvDiff values were defined by the Affymetrix software output. Fold change is defined as the ratio of AvDiff values of RNA derived from PBMCs treated with cytokines over that of untreated PBMCs. Kinetic PCR was used to correlate the mRNA expression values from the Affymetrix gene chips for several genes.

Normalization of the expression data set. The raw expression data for all genes were normalized to the trimmed mean (98%) expression values of the chip and the normalized expression value for all chips were set at 500 (Hu, J. et al., 2000. Ann. N.Y. Acad. Sci., 919:9–15). The genes were also normalized to a set of 20 control (housekeeping) genes that were found to be stable during the various treatments of the samples. The analysis focused on all the genes that were expressed in the PBM cells, and, more specifically, on all genes that were significantly upregulated or downregulated by the in vitro cytokine stimulation and were subsequently reversed by glucocorticoid treatment. A predictive classifier was then applied to these genes. Three separate approaches were taken to search for predictive gene signals: 1) baseline expression values alone; 2) expression values in response to in vitro exposure to cytokines; and 3) expression values in response to cytokines in the presence of GC pre-treatment. To minimize the potential for selection bias, predictions were made for both an independent set and with the cross validation method (Ambroise, C. and McLachlan, G., 2002. Proc. Natl. Acad. Sci. USA, 99:6562–6566).

Classification of drug response phenotypes by naïve Bayesian classifier. A naïve Bayesian classifier (with non-informative prior) (Duda, R. and Hart, P., Pattern Classification and Scene Analysis. 1973, New York: John Wiley) was applied to test if the classification of drug response phenotypes can be achieved by expression values for a few informative genes. The classifier is trained by selecting those genes that are deemed relevant in distinguishing between phenotypes (Kellner, A. et al., Bayesian classification of DNA array expression data. Technical report, Department of Computer Science and Engineering, University of Washington, August, 2000). When a new case is presented to the classifier, the observed attribute value x_(g) for gene g results in the odds that the presented case is glucocorticoid sensitive:

$\frac{P\left( {{x_{g}/G}\; C\; S} \right)}{P\left( {{x_{g}/G}\; C\; R} \right)}$

Combining the probabilities of all genes in the classifier (under the “naïve” assumption of independence) gives the total odds:

$\frac{P\left( {G\; C\; S} \right)}{P\left( {G\; C\; R} \right)} = {\prod\limits_{g}\frac{P\left( {{x_{g}/G}\; C\; S} \right)}{P\left( {{x_{g}/G}\; C\; R} \right)}}$

The attributes, namely AvDiff values or fold changes, were divided into two categories (low, high) by choosing a threshold value that optimally separates phenotypes in the training set for each gene. The probability, P(x|C), was taken as the fraction of training cases of phenotype C with attribute value x. To avoid singularities due to probabilities of zero, a Laplace estimator was used, i.e., an additional case of each phenotype was added to each attribute category.

The genes used for phenotype prediction were selected by their power to separate between the phenotypes using the average of

$\frac{P\left( x_{g} \middle| {G\; C\; S} \right)}{P\left( x_{g} \middle| {G\; C\; R} \right)}$ over training cases with phenotypes GC-S and GC-R, respectively, as gene score. The n genes with the highest total score (i.e., sum of scores for phenotype GC-S and GC-R) were used in the classifier. As attributes, the fold change value between baseline and cytokine induction was used. To avoid spurious fold change values, only genes with AvgDiff values>70 for baseline and cytokine induction for all individuals were considered.

Prediction of an independent patient set using the weighted voting method described by Golub et al. The GCS and GC-R patient cohorts were each randomly split into two cohorts. The random split procedure was performed 10 times with separate analyses each time. A predictor for the first cohort using a weighted voting algorithm similar to that described previously (Golub, T. et al., 1999., Science, 286:531–537) by selecting genes that are deemed most relevant in distinguishing GC responders from non-responders. The weighted voting algorithm makes a weighted linear combination of relevant “informative” genes obtained in the training set to provide a classification scheme for new samples. A brief description of the algorithm follows. The mean (μ) and standard deviation (ε) for each of the two classes (Resistant and Sensitive) in the training set is first calculated. The Euclidean distance between the two classes is then calculated for each gene (x) such that ED_(X)=√(μ_(R)−μ_(S))². The variance of each class is equal to the standard deviation within that class squared, V_(R,S)=ε². Next, the metric used to choose the most informative genes and for the calculation of weight, for each gene, is calculated as follows: Mx=(ED_(x) ²)/(V_(S)+V_(R)). To predict the class of a test sample Y, each gene X casts a vote for each class: W_(XR)=M_(X)√(Y−μ_(R))² and W_(XS)=M_(X)√(Y−μ_(S))². The final class of test Y is found by the lesser of (ΣW_(XR)) and (ΣW_(XS)). Thus, each gene has a vote based on its metric and the class to which its signal is closest. The class with the smallest vote at the end is the predicted class, e.g., the class the test sample is closest to using the Euclidean distance as the measure, is the predicted class. It was then determined whether an accurate prediction of drug response can be achieved by expression values of this limited set of genes for the independent cohort with no prior information using commercially available software as described previously (Shipp, M. et al., 2002. Nat. Med., 8:68–74; Golub, T. et al., 1999., Science, 286:531–537).

Prediction of an independent patient set using the k-Nearest Neighbor algorithm. The k-Nearest Neighbor (k-NN) algorithm (Cover, T. et al., 1967. IT, 13:21–27) was also implemented to predict the class of a sample by calculating the Euclidean distance of the sample to the k “nearest neighbor” standardized samples in “expression” space in the training set. The class memberships of the neighbors are examined, and the new sample is assigned to the class showing the largest relative proportion among the neighbors after adjusting for the proportion of each class in the training set. The marker gene selection process was performed by feeding the k-NN algorithm only the features with higher correlation to the target class. To select genes for use in the predictor, all genes were examined individually and ranked on their ability to discriminate one class from the other using the information on that gene alone. For each gene and each class, all possible cutoff points on gene expression levels for that gene are considered to predict class membership either above or below that cutoff. Genes were scored on the basis of the best prediction point for that class. The score function is the negative logarithm of the p-value for a hypergeometric test (Fisher's exact test) of predicted versus actual class membership for this class versus all others.

Prediction using Leave One Out Cross Validation (LOOCV). In addition to vonfirmation in an independent set of patients, the genes that best separated GC responders from non-responders were selected and tested by cross-validation, wherein one patient was excluded from the data set and then a new predictor was generated from all the genes based on the remaining patients. The new predictor was then used to predict the excluded sample. The gene selection criteria was determined as described above and applied to expression values at baseline or in response to in vitro exposure to cytokines and GC.

Reagents. The cDNA Hu95 gene microarray chips and analysis system, including scanner and computer analysis software, were purchased from Affymetrix, Calif. The RPM-1640 medium was obtained from Gibco BRL (Gaithersburg, Md.). IL-1β and TNFα were obtained from R&D Systems (Minneapolis, Minn.). DEX was purchased from Sigma (St Louis, Mo.).

Results

Characteristics of patients. The patients enrolled were randomly selected from the available family clusters, which included 1185 patients, of whom over 500 were using inhaled glucocorticoids. Of the 96 patients recruited for the study, 60 were determined to be glucocorticoid sensitive and 36 were determined to be glucocorticoid resistant. Each group was split into two cohorts. The first cohort included 46 patients (training set), of whom 30 were GC-S and 18 were GC-R. The additional 46 patients were designated as the independent set, and also included 30 GC-S patients and 18 GC-R patients. The mean maintenance dose of inhaled glucocorticoids in the GC-R group was ˜1,600 mg/day (range 1,000–2,000). In contrast, the GC-S patients required only intermittent or low-dose therapy (≦800 mg/day of Fluticasone or equivalent drug) of inhaled GC. Demographic information, lung function and methacholine challenge values together with atopy status are presented in Table 3. It should be noted that while the argument could be made that the asthma severity level was higher in the GC-R group, the lung function tests results were only slightly lower in the GC-R patients as compared to the GC-S patients. As shown in Table 3, the ratio of males/females and the atopy status were lower in the GC-R group, whereas the mean age was higher. Moreover, 54% GC-S and 31% of the GC-R patients were skin test positive to one or more aeroallergens. No differences were observed in the ratio of T cells, B cells and monocytes between the two groups.

Performance of the Bayesian classifier with gene score selected genes. This study examined whether GC-R asthmatic patients can be identified by specific gene expression profiles in white blood cells, which are different from those obtained in patients who are GC-responders, using a naïve Bayesian classifier. The classifier was trained to select genes that best predicted between the GC-R and GC-S patient groups using all genes from the Affymetrix 12,600 Hu95 gene chip that were defined as being present and had an AvgDiff expression values>70. The latter requirement reduced the total number of genes to 5,011. Thus, the classifier was trained on 5,011 genes with the goal of identifying genes that demonstrated differences between the two groups in either their AvgDiff or fold change expression values at baseline (BL) or in response to cytokine or GC treatment. Fold change mRNA expression values in response to cytokine treatment of 50 genes distinguished a randomly selected subgroup of 14 GC-R and 14 GC-S patients with 82% accuracy. Neither baseline expression values nor fold change expression in response to GC improved the classifier's ability to discriminate between the two patient groups. The prediction of the drug response phenotype was done by the “leave-one-out cross validation” (LOOCV) method, wherein each training case is left out in turn and predicted by a classifier trained on the remaining training cases only. The percentage of the left-out training cases that were predicted correctly is then taken as an estimate of the classifier's accuracy for previously unseen cases. As shown in FIG. 1, 12 of the 14 GC-S and 11 of the 14 GC-R patients are correctly predicted. Moreover, when the classifier was trained on all of the patients, its predictive accuracy of determining the correct GC-response phenotype using the LOOCV method was essentially unchanged or 81% (FIG. 2).

Since these 50 genes discriminated between the drug response phenotypes with high accuracy, they may potentially have stronger power to predict the drug response phenotype for individuals within the group itself. Thus, the ability of these genes to predict the drug response phenotype of a separate cohort of patients that was not used to train the classifier was examined. As shown in FIG. 3, when the classifier was trained on the 40 GC-S versus GC-R patients, and then used to predict the an independent patient set, the predictive accuracy obtained rose to 86%.

Fifty genes contributed to the predictive power that discriminated between GC responders and non-responders. These genes are categorically displayed in Tables 1, 2A and 2B. Data on 14 control subjects with unknown GC-response profile is also included. As shown, the pattern of mRNA expression in the control group was comparable to that of the GC-S patients. This is not surprising since up to 90% of asthma patients in general are GC-responders. The genes in each category (notably CAM/ECMs, cell signaling/metabolism molecules, transcription factors and ESTs) are identified by their GenBank accession numbers, and their respective magnitudes of mean fold change of altered mRNA expression in response to cytokines.

To ensure that the gene signals that most accurately discriminate between GC responders and non-responders were captured, the groups were expanded to 96 patients (60 GC-S and 36 GC-R) and the weighted voting and k-NN algorithms were applied to both randomly split cohorts of GC-S and GC-R patients with and without cross validation.

Apart from the genes in Tables 1, 2A and 2B, 15 additional genes shown in Table 4 increased the predictive accuracy for the independent set to 86%, and the accuracy of discriminating GC responders from non-responders amounted to 89% with cross validation in all patients.

Discussion

While drug treatment remains a mainstay of medicine, in most cases a given drug has little or no effect in the majority of patients, or unforeseen serious side effects occur. For the patient this represents a dangerous and potentially life-threatening situation and, at the societal level, adverse drug reactions represent a leading cause of disease and death. Genetic variation often underlies poor response or side effects. Indeed, there already exist several examples of such correlation, including but not limited to patients variability in response to Dicumarol, Warfarin or Isoniazid due to polymorphisms in the Cyt P450 gene that confer rapid versus slow acetylating of these drugs. Given that these genetic variations may be reflected in differences in regulatory functions of these genes, variability in the mRNAs and/or protein expressions of these genes would be expected. Pharmacogenomics holds the promise that one may soon be able to profile variations between individuals' genetic makeup that accurately predict responses to drugs, addressing both efficacy and safety issues. In this connection, the ability to analyze the expression levels of thousands of genes in a single assay by DNA microarray technology provides a powerful screen of multiple molecular gene pathways, simultaneously. Thus, high-throughput gene array assay has the potential of identifying differential expression in genes encoding for various enzymes, kinases, ion channels, and other cell signaling molecules that determine individual's variation in response to drugs.

To address these issues, this study examined differences in gene expression in freshly isolated PBMCs isolated from GC-R and GC-S asthmatic patients, using high-density DNA microarray analysis. The results demonstrate that glucocorticoid sensitivity can be predicted with 86% accuracy.

It is widely accepted that glucocorticoids (GCs) are the most effective drugs available in asthma therapy. In individuals who are sensitive, inhaled GC has been shown to have a relatively low capacity to activate transcription within PBMCs at concentrations found in plasma and their action is thought to mainly occur within the lung. This finding is in agreement with the restricted systemic side effects at low or intermittent doses, whereas the relative abilities of GC to trans-repress transcription factor activities, such as AP-1 and NF-κB, in the airways is in agreement with their relative clinical efficacy. In contrast, GC-resistance has been defined by the lack of a response to a prolonged course of high dose (>1 mg/kg/day) oral glucocorticoid such as prednisone. Since most patients with asthma are being treated with inhaled GC, a new definition referring to GC-dependent/resistant asthma has emerged taking into account the inhalation route in the use of the drug (Leung, D. and Chrousos, G., Am. J. Resp. Crit. Care Med. 2000. 162:1–3). In light of these issues, a recommendation to define GC-resistant asthma as a condition where there is incomplete response to high doses of inhaled GC (i.e., >2,000 mg/day) was followed (Gagliardo, R. et al., Am. J. Resp. Crit. Care Med. 2000. 162:7–13). While clear separation between patients with high level of GC-dependent versus GC-resistant asthma is not always possible clinically, there is no doubt that both of these groups present a challenging clinical problem that is highly costly to the health care system. Sub-optimal responses to steroids often lead to prolonged courses of high-dose GC therapy accompanied by serious adverse effects together with persistent airway compromise. Patients with GC-resistant asthma present an ongoing inflammation of the airways despite persistent treatment with high doses of GC. This would be consistent with the high degree of airway hyperresponsiveness detected in our GC-R patients as reflected by the low methacholine concentration values in Table 3.

Given the pathobiologic processes involved in asthma, as a first approximation, it is reasonable to consider GC-insensitive and GC-dependent asthma as a part of the same pathogenic process. Thus, in view of potential heterogeneity and complexity of mechanisms contributing to GC-resistant/dependent asthma and its potential impact on the natural history of chronic asthma, it is noteworthy that no differences in the gene expression profiles of our GC-dependent study patients with moderate to severe GC-resistance, with respect to their GC resistant trait, were found.

In this study, a gene-scoring approach to identify genes that provide predictive power was used. As shown in Table 1, the 40 genes selected differed in their signal intensities of mRNA expression between the two groups and correlated strongly with the patients response phenotypes to inhaled glucocorticoids. Moreover, as shown in FIG. 1, when a Bayesian classifier was trained on a subset of 14 GC-S and 14 GC-R patients the predictive accuracy of the classifier to discriminate between these two patient groups was 82%. When the classifier was trained on 40, the accuracy of the classifier to predict the remaining independent patients was as high as 86% (FIG. 2). Thus, by almost doubling the number of patients that were used to train the classifier, the predictive power of the study increased further. The latter observation has two important implications: 1) it suggests that the predictive accuracy of the classifier might improve further if additional patients are included; and 2) the approach using the naïve Bayesian classifier when applied to the initial data set of 14 GC-S and 14 GC-R patients, provided sufficient power to predict gene expression profiles of GC-R and GC-S asthmatics with over 80% accuracy. It is noteworthy that among these genes are numerous cytokine/chemokine-related genes, transcription factors and cell signaling molecule genes (Tables 1, 2A and 2B). No doubt many of these genes may turn out to be critical regulatory genes of GC responsiveness independent of asthma per se.

Genes with predictive power in discriminating between glucocorticoid sensitive and glucocorticoid responsive asthmatics are listed in Table 1. The 40 genes that most accurately discriminate between glucocorticoid-responsive (GC-R) and glucocorticoid-sensitive (GC-S) asthmatics using fold change mRNA expression values following stimulation with cytokines are shown. The values expressed as mean±SEM for the groups. Also shown are comparable values in 14 non-asthmatic (control) subjects with unknown GC-response status. Expression profiles including these genes predict GC-R and GC-S patients with over 80% accuracy.

TABLE 1 Mean Fold change in RNA Expression of Informative Genes GCR GCS Control Name/ mean ± mean/± mean/± Gene Category GenBank sem sem sem PV Transcription Factors Zinc finger Protein ZNF267/X78925 3.05 ± 0.45 5.41 ± 0.99 3.76 ± 0.30 0.94 267 Zinc finger Protein ZNF189/AF025770 2.98 ± 0.38 4.12 ± 0.61 2.92 ± 0.44 0.88 189 Interferon-stimulated ISGF-3/M97935 1.50 ± 0.80 4.38 ± 0.78 2.28 ± 0.59 0.82 gene factor 3 N-myc and STAT Nmi/U32849 2.28 ± 0.25 1.41 ± 0.20 1.50 ± 0.09 0.81 interactor Zinc finger helicase ZFH/U91543 −1.39 ± 0.20   −2.04 ± 0.11   −1.25 ± 0.19   0.79 Zinc finger Protein ZNF145/AF060568 −3.23 ± 0.88   −7.79 ± 2.59   −5.94 ± 1.65   0.71 145 Interferon-induced IFP35/U72882 2.66 ± 0.39 3.56 ± 0.27 2.70 ± 0.30 0.69 leucine zipper protein C terminal binding CtBP/U37408 −1.36 ± 0.20   −1.73 ± 0.30   −1.43 ± 0.09   0.64 protein Cell signaling/metabolism PDGF receptor beta-like PRLTS/D37965 0.51 ± 0.41 2.99 ± 0.58 0.67 ± 0.48 0.94 tumor suppressor Sterol carrier protein-X; SCP-X; SCP-2/U11313 −2.44 ± 0.20   −1.61 ± 0.35   −1.86 ± 0.11   0.94 sterol carrier protein-2 G protein-linked GPRG/L42324 4.14 ± 1.35 8.46 ± 3.50 3.85 ± 1.16 0.94 receptor gene Nuclear Factor kappa NFkB/M58603 4.10 ± 0.26 2.68 ± 0.25 3.12 ± 0.19 0.93 B subunit Allograft AIF1/U49392 −4.48 ± 0.49   −2.24 ± 0.54   −4.54 ± 0.50   0.88 inflammatory factor 1 c-syn protooncogene FYN/M14333 1.87 ± 0.09 1.55 ± 0.11 1.74 ± 0.04 0.86 Small nuclear SNRPA/M60784 −1.75 ± 0.10   −3.76 ± 1.34   −1.80 ± 0.09   0.86 ribonucleoprotein polypeptide A 2′5′ Oligoadenylate none/M87434 2.69 ± 0.35 4.52 ± 0.59 2.56 ± 0.49 0.81 synthetase Rab GTPase HSRANGAP1/X82260 2.42 ± 0.13 1.89 ± 0.25 1.94 ± 0.31 0.79 activating protein 1 Vasoactive intestinal VIPR1/X77777 −3.76 ± 0.52   −11.65 ± 2.37    −6.24 ± 1.02   0.79 peptide receptor 1 NADH-ubiquinone NDUFV1/AF053070 −1.54 ± 0.10   −1.89 ± 0.31   −1.61 ± 0.16   0.79 dehydrogenase 51 kDa subunit SH3BGR-like protein SH3BGRL/AF042081 −2.04 ± 0.20   −1.22 ± 0.29   −1.88 ± 0.09   0.75 SRC Kinase SKAP55/Y11215 −6.59 ± 0.48   −12.60 ± 2.29    −13.36 ± 5.40    0.71 associated phosphoprotein 55K Retinal short-chain retSDR1/AF061741 −0.61 ± 0.36   0.24 ± 0.40 0.33 ± 0.70 0.71 dehydrogenase/reductase NAD (H)-specific none/Z68907 −1.58 ± 0.05   −1.67 ± 0.30   −1.60 ± 0.06   0.71 isocitrate dehydrogenase g Lysosome-associated DCLAMP/AB013924 70.09 ± 11.70 128.82 ± 24.64  96.54 ± 13.97 0.69 membrane glycoprotein Aryl carbon receptor AHR/L19872 −1.96 ± 0.38   −0.82 ± 0.37   −0.24 ± 0.45   0.69 Ser/Thr kinase 10 lok/AB013924 −1.49 ± 0.06   −1.47 ± 0.23   −1.60 ± 0.08   0.64 Docking protein 2 DOK2/AF034970 −2.08 ± 0.09   −2.24 ± 0.54   −2.55 ± 0.16   0.64 Ecto-5-prime- CD73/X55740 −2.38 ± 0.20   −4.22 ± 0.89   −3.16 ± 0.94   0.64 nucleotidase Signal-induced SIPA1/AB005666 −1.68 ± 0.29   −2.76 ± 0.51   −1.85 ± 0.15   0.57 proliferation-associated gene 1 Phospholipid hMmTRA 1b/AB006746 3.80 ± 0.52 6.66 ± 0.82 3.94 ± 0.44 0.56 Scramblease Misc Fc fragment of FCRER1A/X06948 −17.64 ± 1.55    −10.44 ± 2.36    −16.43 ± 4.08    0.94 IgEalpha Histone stem-loop SLBP/U75679 1.07 ± 0.17 1.21 ± 0.31 0.69 ± 0.36 0.94 binding protein Interferon induced IFI56/M24594 10.63 ± 2.73  23.63 ± 3.73  10.09 ± 2.24  0.88 protein 56 Neuropathy target NTE/AJ004832 −1.42 ± 0.25   −2.34 ± 0.13   −1.37 ± 0.24   0.86 esterase Interferon induced IFP41/L22342 1.32 ± 1.32 2.38 ± 0.23 1.44 ± 0.34 0.75 protein 41 Poly A binding PAPB2/AF026029 −1.36 ± 0.06   −2.02 ± 0.24   0.68 ± 0.31 0.71 protein II Galectin 2 GaI2/AL022315 −56.71 ± 7.96    −23.46 ± 5.90    −44.87 ± 8.65  0.69 Actin related protein ARPC2/U50531 −2.79 ± 0.65 −7.05 ± 1.37 −3.25 ± 1.36 0.57 complex 2/3 subunit 2 CD1c Thymocyte CD1c/M28827 −5.11 ± 0.62   −3.56 ± 0.34   −4.73 ± 0.74   0.56 antigen cDNA clone 24538 mRNA none/AF055030 0.84 ± 0.28 1.52 ± 0.24 1.42 ± 0.11 0.94

Tables 2A and 2B include 45 genes that were either upregulated or downregulated by IL-1β/TNFα therapy and were reversed, at least in part, by pre-treatment with GC, and that most accurately predicted glucocorticoid sensitivity using expression values generated following treatment with cytokines. Table 2A shows expression levels in cells treated with IL-1β/TNFα in the presence of DEX and Table 2B shows expression levels of cells treated with IL-1β/TNFα alone.

TABLE 2A Genes upregulated by IL-1β/TNFα and repressed by DEX IL1/TNFα treatment: With DEX GCR GCS Name/ mean ± mean/± Gene Category GenBank sem sem P-value Inflammatory cell regulators Interferon-inducible 56 Kd IFI56/ −816 ± 91 −1920 ± 240  <0.0001 M24594 Interleukin 1β IL1B/ −3792 ± 501   1605 ± 1339 0 M15330 Interferon gamma treatment inducible IFNIND/ −12235 ± 955  −5432 ± 1553 0 mRNA M26683 CD6 antigen CD6/ −1128 ± 192    58 ± 265 0.001 X60992 Interferon regulatory factor 7B IRF7/ −908 ± 91 −876 ± 175 0.863 U53831 TNFα receptor associated EB16/ −2061 ± 185 −1419 ± 109  0.0133 factor 1 U19261 TNFα receptor CD120b/ −3321 ± 520 −1283 ± 407  0.006 M32315 TNFα receptor superfamily WSL/ −3321 ± 506 −1591 ± 402  0.0185 Y09392 Leukocyte surface aminopeptidase N CD13/  −983 ± 122 −247 ± 106 0 M22324 Cytokine (GRO-γ) SCYB3/ −2135 ± 345 −672 ± 59  0.001 M36821 Interferon-inducible peptide (6–16) gene 16-jun/ −1233 ± 151 −2094 ± 424  0.041 U22970 Cytokine (GRO-β) SCYB2/ −2934 ± 374 −1116 ± 384  0.003 M36820 Interferon-β-2a IL6/ −7784 ± 683 −4493 ± 788  0.002 X04430 OX40 cell surface antigen OX40/  −600 ± 103 −230 ± 47  0 S76792 Monocyte/macrophage lg-related receptor MIR10/ −626 ± 73 −238 ± 57  0 AF004231 Small inducible cytokine subfamily C2 SCYC2/ −417 ± 80 −118 ± 76  0.0156 D63789 Interleukin-1 receptor antagonist IL1RA/ −10750 ± 966  −5445 ± 1457 0.004 X52015 Urokinase-type plasminogen receptor CD87/ −3834 ± 530 −1504 ± 272  0.002 U09937 Activation mRNA Act-2/ −4202 ± 494 −1333 ± 572  0.001 J04130 Macrophage migration inhibitory factor MIF/ −1222 ± 383 −170 ± 238 0.0365 L19686 Monocyte secretory protein JE/ −7380 ± 860 −4302 ± 960  0.0331 M28225 Interferon stimulated gene HEM45 HEM45/ −1600 ± 367 −424 ± 368 0.039 U88964 Membrane glycoprotein 4F2 antigen heavy CD98/  −687 ± 115 −301 ± 124 0.0374 chain J02939 Interferon induced protein p78 p78/ −3547 ± 345 −1491 ± 346  0 M33882 CD44 isoform RC CD44/ −1886 ± 170 −1019 ± 134  0.001 AF098641 Interferon-induced 17-kDa/15-kDa protein ISG15/ −5244 ± 677 −2526 ± 611  0.008 M13755 Cell Signaling/Metabolism Plasminogen activator-inhibitor 2 PAI-2/ −7538 ± 623 −2683 ± 648  <0.0001 Y00630 PROS-27 PROS-27/ −2771 ± 318 −361 ± 402 0 X59417 Cyclin dependent kinase inhibitor 1A p21/ −2463 ± 315 −885 ± 151 0 U03106 Lymphocyte G0/G1 switch gene G0S2/ −3788 ± 420 −1657 ± 275  0 M69199 Hpast HPAST/ −1532 ± 187 −675 ± 139 0.002 AF001434 Apoptosis inhibitor/AP homolog B AP11/  −722 ± 315 −342 ± 151 0.002 (MIHB) U37547 Insulin induced protein 1 INSIG1/ −2778 ± 273 −1797 ± 204  0.0117 U96876 pim-1 oncogene pim-1/ −1937 ± 245 −1197 ± 152  0.0205 M16750 Cyclic AMP-responsive element modulator CREM/ −2225 ± 238 −1435 ± 149  0.0207 S68134 Glucose transporter-like protein-lll GLUT3/ −3564 ± 801 −1422 ± 512  0.0366 M20681 Human cyclooxygenase-2 hCox-2/ −1731 ± 222 −2333 ± 389  0.167 U04636 CAM/ECM molecules HB14 CD83/ −4434 ± 73  −2492 ± 78  0 Z11697 Adhesion molecule ninjurin NINJ1/ −1977 ± 315 −472 ± 249 0.001 U91512 Tissue inhibitor of metalloproteinases 1 TIMP1/ −4723 ± 500 −2237 ± 483  0.002 D11139 Elastase specific proteinase inhibitor ELAFIN/ −1448 ± 462 −292 ± 199 0.0449 L10343 Transcription Factors Nef-associated factor 1β Naf1beta/ −1523 ± 467   126 ± 512 0.0268 AJ011896 Thyroid receptor interactor TRIP 14/ −635 ± 67 −798 ± 83  0.1357 L40387 Basic helix-loop-helix transcription factor Musculin/ −1867 ± 177 −1209 ± 116  0.006 AF087036 p50-NFκβ P50-NF-kB/  −890 ± 174 −321 ± 105 0.0122 S76638

TABLE 2B Genes upregulated by IL-1/TNF-α and repressed by DEX IL1/TNFα treatment: Without DEX GCR GCS Name/ mean ± mean/± Gene Category GenBank sem sem P-value Inflammatory cell regulators Interferon-inducible 56 Kd IFI56/ 1283 ± 215 3370 ± 372 <0.0001 M24594 Interleukin 1β IL1B/  9905 ± 1021  595 ± 1430 <0.0001 M15330 Interferon gamma treatment inducible IFNIND/ 17349 ± 784  11063 ± 998  <0.0001 mRNA M26683 CD6 antigen CD6/ 2472 ± 179  806 ± 195 <0.0001 X60992 Interferon regulatory factor 7B IRF7/ 1413 ± 129 3074 ± 187 <0.0001 U53831 TNFα receptor associated factor 1 EB16/ 5191 ± 188 3643 ± 267 0 U19261 TNFα receptor CD120b/ 6742 ± 513 3453 ± 877 0 M32315 TNFα receptor superfamily WSL/ 6560 ± 462 3452 ± 626 0 Y09392 Leukocyte surface aminopeptidase N CD13/ 2988 ± 304 1523 ± 184 0.001 M22324 Cytokine (GRO-γ) SCYB3/ 3228 ± 392 1431 ± 284 0.001 M36821 Interferon-inducible peptide (6–16) gene 16-jun/ 1870 ± 317 3656 ± 362 0.002 U22970 Cytokine (GRO-β) SCYB2/ 5431 ± 653 2267 ± 509 0.002 M36820 Interferon-β-2a IL6/ 12870 ± 630  9257 ± 863 0.002 X04430 OX40 cell surface antigen OX40/ 1157 ± 88  588 ± 28 0.002 S76792 Monocyte/macrophage lg-related receptor MIR10/ 1492 ± 147  780 ± 157 0.003 AF004231 Small inducible cytokine subfamily C2 SCYC2/  483 ± 115  28 ± 90 0.006 D63789 Interleukin-1 receptor antagonist IL1RA/ 13395 ± 1481  8202 ± 1598 0.0168 X52015 Urokinase-type plasminogen receptor CD87/ 4180 ± 663 2054 ± 414 0.0197 U09937 Activation mRNA Act-2/  8697 ± 1013  3900 ± 1860 0.026 J04130 Macrophage migration inhibitory factor MIF/ 2250 ± 705  296 ± 356 0.0271 L19686 Monocyte secretory protein JE/ 9970 ± 999 6785 ± 534 0.0369 M28225 Interferon stimulated gene HEM45 HEM45/ 6801 ± 713 4768 ± 553 0.0474 U88964 Membrane glycoprotein 4F2 antigen heavy CD98/ 3073 ± 194 2472 ± 243 0.0662 chain J02939 Interferon induced protein p78 p78/ 7155 ± 826 5241 ± 503 0.0777 M33882 CD44 isoform RC CD44/ 3651 ± 264 3121 ± 195 0.0962 AF098641 Interferon-induced 17-kDa/15-kDa protein ISG15/  8821 ± 1574 6128 ± 993 0.1801 M13755 Cell Signaling/Metabolism Plasminogen activator-inhibitor 2 PAI-2/ 9427 ± 717 5832 ± 531 0.001 Y00630 PROS-27 PROS-27/ 5853 ± 316 3557 ± 443 0 X59417 Cyclin dependent kinase inhibitor 1A p21/ 3544 ± 492 1975 ± 261 0.0123 U03106 Lymphocyte G0/G1 switch gene G0S2/ 1403 ± 737 1347 ± 548 0.9539 M69199 Hpast HPAST/ 2370 ± 263 1545 ± 272 0.0418 AF001434 Apoptosis inhibitor/AP homolog B AP11/ 1826 ± 137 1885 ± 150 0.7754 (MIHB) U37547 Insulin induced protein 1 INSIG1/ 4414 ± 353 3201 ± 285 0.0175 U96876 pim-1 oncogene pim-1/ 2347 ± 411 1947 ± 511 0.5462 M16750 Cyclic AMP-responsive element modulator CREM/ 4875 ± 216 4613 ± 411 0.541 S68134 Glucose transporter-like protein-lll GLUT3/ 5396 ± 930 2914 ± 713 0.0476 M20681 Human cyclooxygenase-2 hCox-2/ 1735 ± 302 2683 ± 513 0.1195 U04636 CAM/ECM molecules HB14 CD83/ 5991 ± 138 2892 ± 150 <0.0001 Z11697 Adhesion molecule ninjurin NINJ1/ 4607 ± 401 2359 ± 264 0 U91512 Tissue inhibitor of metalloproteinases 1 TIMP1/  9451 ± 1006 5574 ± 611 0.005 D11139 Elastase specific proteinase inhibitor ELAFIN/ 2744 ± 768  577 ± 174 0.0166 L10343 Transcription Factors Nef-associated factor 1β Naf1beta/ 6890 ± 505 3412 ± 314 <0.0001 AJ011896 Thyroid receptor interactor TRIP14/ 1046 ± 130 1488 ± 167 0.0452 L40387 Basic helix-loop-helix transcription factor Musculin/ 2832 ± 297 2145 ± 109 0.0489 AF087036 p50-NFκβ P50-NF-kB/ 2394 ± 169 1995 ± 206 0.1487 S76638

As shown in Table 3, the ratio of males/females and the atopy status were lower in the GC-R group, whereas the mean age was higher. While several studies have reported association between the X chromosome and the asthma phenotype (Kauppi P. et al., Eur. J. Hum. Genet. 2000. 10:788–92; Heinzmann A. et al., Hum. Mol. Genet. 2000. 9:549–59; Ahmed S. et al., Exp. Clin. Immunogenet. 2000. 17:18–22), extended epidemiological studies are needed to confirm if such an association exists between the GC-R phenotype and asthma. Likewise, 54% of the GC-S patients in this study were skin test positive to one or more aeroallergens compared to 31% of the GC-R patients. In addition, the average total IgE levels were higher in the GC-S group. About 70% of patients in some European countries and areas of the United States are atopic (Eggleston P. and Bush R., J. Allergy Clin. Immunol. 2001 107:S403–5) compared to about 50% of asthmatics in Iceland, as determined by positive skin tests or elevated IgE levels (The European Community Respiratory Health Survey Group. Am. J. Resp. Crit. Care Med. 1997. 156:1773–1780). Whether atopic asthmatic patients are less likely to have GC-R asthma compared to non-atopic patients remains to be determined. While interference from these variables on the results cannot be excluded, the predictive power of the patient's age, sex or atopy status was lower (and not statistically significant) when compared to the power developed from the genes under study.

These results provide the basis for unraveling the mechanisms that contribute to the development of GC-resistance, and can allow for the development of new therapeutic approaches. For example, one of skill in the art can perform linkage studies on patients for GC-R and GC-S clusters using the Icelandic Genealogy Database. By linking patients with GC-R and GC-S asthma into large family pedigrees based on clinical measures of GC-responsiveness, a genome-wide linkage analysis can be performed. Secondly, by searching for mRNAs expression profiles that predict GC-R versus GC-S asthma in a larger cohort of patients using DNA array technology, one of skill in the art can categorize patients with unknown GC profile into GC-S and GC-R patients with high accuracy independent of whether the patient has been taking glucocorticoids or whether the clinical response has been determined based on the expression profile alone (e.g., if similar to the expression profiling of responders versus non-responders, respectively).

Table 3. Demographic, lung function, metacholine challenge and total IgE values in glucocorticoid-sensitive (GC-S) and GC-resistant(R) patients. Values expressed are mean+/−SE for the groups.

TABLE 3 Characteristics GC-S <Asthma> GC-R n 61 35 Mean age (yr) 43 ± 16 54 ± 19 Sex ratio (M/F) 30/70 20/80 Mean dose GC 450 (200–800) 1,610 (1,000–2000) (μg/day) FVC (% predicted) 84 ± 19 75 ± 15 liters 3.8 ± 1.0 2.2 ± 0.8 FEV1 (% predicted) 82 ± 20 81 ± 8  liters 3.2 ± 1.0 3.3 ± 0.3 FEV1/FVC ratio 70 ± 10 72 ± 12 Positive skin tests 54% 31% MCh challenge (mg/L) 3.4 ± 1.2 0.7 ± 1.1 Total IgE (IU/L) 78 38

To ensure that the gene signals that most accurately discriminate GC responders from non-responders are captured, the study was expanded to include 96 patients (60 GC-S and 36 GC-R) and randomly split cohorts of GC-S and GC-R patients with and without cross validation were analyzed.

The results demonstrate that apart from genes listed in Tables 1, 2A and 2B, 15 additional genes, listed in Table 4A, were identified with the split cohort method and 4 genes, listed in Table 4B, by the cross validation method, which increased the predictive accuracy to 86% and 89% for the independent set and cross validation method, respectively. These genes play a key role in immune function, signal transduction processes and in apoptosis, all of which are highly relevant functions to the mechanisms of glucocorticoid responsiveness in asthma.

TABLE 4A Induction Independent Mean Mean GenBank Resistant Std Dev Sensitive Std Dev Z11697 1.8724782 0.33308947 1.58067  0.37548256 AI950382 1.3549194 0.22042978 1.030298  0.2444671  L78440 1.5259613 0.30956995 1.0863237 0.34783307 U19261 1.7525064 0.4171106  1.3473015 0.3836438  M80244 1.9161059 0.56795007 1.1932454 0.40928963 AB023205 2.1412642 0.9683697  1.55956  0.7753593  M58603 1.6857204 0.40931362 1.2413435 0.41113004 L05424 1.8005482 0.49951264 1.3440721 0.3783   R38263 1.4709008 0.3948905  1.2489537 0.32604253 L03411 1.2816724 0.18822539 1.1264083 0.25873253 D63789 1.6949914 0.78736067 1.2824732 0.57320374 X52425 1.4263631 0.3121133  1.1701844 0.32265002 L11329 1.6712576 0.45524144 1.2668103 0.39379898 U48807 2.149289  1.1046098  1.4393674 0.74329394 D79991 1.2781677 0.26072037 1.1245298 0.2646311 

TABLE 4B Induction Cross Validation Mean Mean GenBank Resistant StdDev Sensitive StdDev AB009010 0.7325509 0.3312249 1.4231814 0.5362498 L78440 1.575848 0.33741876 1.1237841 0.30317423 U43185 2.1323266 0.40701827 1.4901624 0.52416813 AB020630 1.6486217 0.2412822 1.2882963 0.31817988

Example 2

Apart from genes that demonstrated predictive accuracy in the around 80%, a large number of genes were found to be differentially expressed between the two patient groups. Of those, the genes listed in Tables 5A–E contributed to the predictive signal that was found. These genes were identified by applying the biological approach of the experimental set up (i.e., genes upregulated or downregulated by IL-1β/TNFα and reversed, at least in part by GC). The ultimate value of the genes in Tables 5A–5E with respect to glucocorticoid prediction will be known when these genes have been validated in a larger cohort of patients.

TABLE 5A BASELINE EXPRESSION VALUES GC resistant GC sensitive (n = 30) (n = 66) Gene (Genbank, Map) MEAN SEM MEAN SEM P value Perforin 1 (M28393, 6085.97 417.13412 5250.672 208.2762 0.026 10q22) Tumor necrosis factor 4043.2695 172.13469 3606.906 126.01189 0.047 receptor 2 (75 kD) (M32315, 1p36.3–p36.2) LPS-induced TNF-alpha 4149.5264 339.81314 3394.3865 152.35988 0.018 factor (AF010312, 16p13.3–p12) Defensin (L12691, 8) 15084.323 1903.8826 10559.057 1213.1726 0.045 KIAA0542 protein 4974.1274 1201.7789 8108.8516 872.0196 0.004 (AB011114, 22q12.2) Beta 2-microglobulin 14026.682 3886.1045 28540.09 3071.3684 0.023 (S82297, 15q21–q22.2) Granzyme B precursor 3153.3257 372.53757 2298.1396 185.16092 0.01 (M57888, 14q11.2) Thymosin, beta 4 21235.469 2459.579 28695.383 2114.4946 0.042 (M17733, Xq21.3–q22) HLA class I heavy chain 23571.922 2747.8547 31607.03 2214.8435 0.042 (X58536, 6p21.3) Elongation factor EF-1- 18835.99 3337.1677 30409.002 2609.5315 0.034 alpha (J04617, 6q14) EST (AI526078, 18714.736 1704.359 24808.363 1547.6501 0.043 20q13.3) CD98 (M80244, 798.8602 93.80168 642.308 47.76093 ns** 16q24.3) Nuclear factor kappa-B 1118.8152 131.4039 1171.212 94.15151 ns** DNA binding subunit (M58603, 4q24) Signal transducer and 2115.2861 177.11328 2072.295 94.97464 ns** activator of transcription 4 (L78440, 2q32.2–q32.3) Nef-associated factor 1 2744.2788 242.81891 2588.429 162.65399 ns** (AJ011896, 5q32–q33.1) CD44 antigen (L05424, 1856.1018 95.87001 1778.6385 104.54533 ns** 11p13) (2′–5′)oligoadenylate 337.42038 18.820648 383.4079 54.559353 ns** synthetase (AJ225089, 12q24.2) Interferon regulatory 820.22296 40.891983 803.94714 37.65985 ns** factor 7 isoform a (U53831, 11p15.5) Interferon-inducible 692.43274 53.86486 769.75037 61.446373 ns** protein p78 (M33882, 21q22.3) interferon inducible 1-8U 10073.882 1202.4331 10348.615 847.59143 ns** gene (X57352, 11) Interferon stimulated 2559.649 120.11462 2733.2332 64.77266 ns** gene (20 kD) (U88964, 15q26) Interferon-stimulated 2001.0354 107.30703 2131.9282 121.11149 ns** protein, 15 kDa (m13755, 1) CD44 isoform RC 1635.3041 92.87121 1546.3922 101.99627 ns** (AF098641, 11p13) CD83 antigen (Z11697, 4003.7795 509.4784 2995.624 343.3428 ns** 6p23) Interferon stimulatable 1222.6351 87.984886 1198.6647 74.25269 ns** response element (U22970, 1p36) TNF receptor-associated 1024.6047 76.8937 970.14984 68.22721 ns** factor 1 (U19621, 9q33–q34) Adhesion molecule 952.9832 108.31899 819.72675 72.15633 ns** ninjurin 1 (U01512, 9q22) TNF receptor-associated 602.10754 55.548782 552.6948 36.47862 ns** factor 1 (U19261, 9q33–q34) Transcription factor 1047.2883 84.6747 1043.8727 51.559406 ns** ISGF-3 (M97936, 2q32.2) interleukin-7 receptor 1293.5005 221.00233 1776.9689 196.31573 ns** (AF043129, 5p13) Interleukin 8 9098.902 881.07416 7048.5723 759.5493 ns** (M28130, 4q13–q21) GRO2 oncogene 3172.951 401.67627 2370.0527 298.47812 ns** (M36820, 4q21) Interferon-induced 242.70598 46.875942 299.46494 47.465668 ns** protein with tetratricopeptide repeats 1 M24594, 10q25–q26) interferon-induced 335.2246 98.50067 348.74557 38.996685 ns** protein with tetratricopeptide repeats 4 (AF026939, 10q24) Macrophage colony 3976.1692 148.23972 4297.3677 118.11726 ns** stimulating factor 1 (M37435, 1p21–p13) Ras inhibitor 103.64886 51.292507 117.76897 47.070602 ns** (M37190, 20) Small inducible cytokine 176.61205 47.855843 356.2777 170.02516 ns** A2 (M28225, 17q11.2–q21.2) Cyclooxygenase-2 1282.8784 245.26799 976.03503 140.18762 ns** (U04636, 1q25.2–q25.3) Transforming growth 665.96454 39.320732 684.6929 31.560013 ns** factor, beta receptor II (70–80 kD) (D50683, 3p22) Cyclic AMP-responsive 571.4737 76.16772 583.9707 86.563515 ns** element modulator beta isoform (S68134, 10p12.2–p11.1) Interleukin 1 receptor 2650.151 386.6683 2049.2832 280.7141 ns** antagonist (X52015, 2q14.2) Interleukin 6 (X04430, 218.93637 79.35666 372.81815 181.83186 ns** 7p21) Nef-associated factor 1 1628.6475 86.34723 1530.4146 92.62164 ns** (AJ011896, 5q32–q33.1) Small inducible cytokine 121.53884 8.26777 149.0766 20.418268 ns** A7 (X72308, 17q11.2–q12) Small inducible cytokine 227.42393 82.810646 527.6503 265.94244 ns** A2 (M26683, 17q11.2–q21.1)

TABLE 5B IL-1/TNF-INDUCED EXPRESSION VALUES GC resistant GC sensitive (n = 30) (n = 66) Gene MEAN SEM MEAN SEM P value Perforin 1 5614.334 394.1253 4072.83 214.3326 <0.0001 Tumor necrosis factor 10050.87 586.6837 7283.63 440.3941 0.0004 receptor 2 (75 kD) (M32315, 1p36.3–p36.2) LPS-induced TNF-alpha 5459.401 351.0782 4387.12 170.5484 0.001 factor (AF010312, 16p13.3–p12) Defensin (L12691, 8) 8846.362 1376.016 4290.63 844.5093 0.009 KIAA0542 protein 2017.074 946.1356 8857.14 1124.027 0.01 (AB01114, 22q12.2) Beta 2-microglobulin 6333.009 3069.832 28707 3191.635 0.01 (S82297, 15q21–q22.2) Granzyme B precursor 1746.806 232.9423 957.865 111.5382 0.016 (m57888, 14q11.2) Thymosin, beta 4 18450.77 2309.75 31440.1 2229.507 0.02 (m17733, Xq21.3–q22) HLA class I heavy chain 17262.1 2654.092 34519.5 2601.363 0.02 (X58536, 6p21.3) Elongation factor EF-1-alpha 15154.59 2826.921 35443.6 2869.217 0.03 (J04617, 6q14) EST (AI526078, 20q13.3) 15054.68 1528.611 25434.4 1606.93 0.03 CD98 (M80244, 16q24.3) 5682.373 365.3485 4127.05 234.5741 <0.0001 Nuclear factor kappa-B DNA 7222.51 338.3345 5655.56 215.4376 <0.0001 binding subunit (M58603, 4q24) Signal transducer and 7142.53 347.9236 5567.9 216.9498 <0.0001 activator of transcription 4 (178440, 2q32.2–q32.3) Nef-associated factor 1 10042.95 525.3091 8829.79 312.5778 <0.0001 (AJ011896, 5q32.q33.1) CD44 antigen (L05424, 6209.805 263.4941 5019.8 163.5661 <0.0001 11p13) (2′–5′)oligoadenylate 3611.673 259.3965 1756.89 225.7237 <0.0001 synthetase (AJ225089, 12q24.2) Interferon regulatory factor 2841.19 237.6626 1681.36 160.3967 <0.0001 7 isoform a (U53831, 11p15.5) Interferon-inducible protein 4426.94 533.6766 1780.97 325.196 <0.0001 p78 (M33882, 21q22.3) interferon inducible 1-8U 5763.147 1332.153 4255.33 398.2605 <0.0001 gene (X57352, 11) Interferon stimulated gene 6082.724 624.8853 4290.37 330.7253 <0.0001 (20 kD) (U88964, 15q26) Interferon-stimulated protein, 10922.46 1395.841 4956.73 622.4254 <0.0001 15 kDa (M13755, 1) CD44 isoform RC (AF09861, 5146.13 287.7974 3879.53 144.6777 <0.0001 11p13) CD83 antigen (Z11697, 9242.953 350.8769 7918.24 339.6289 <0.0001 6p23) Interferon stimulatable 3203.982 495.8736 1631.66 281.1401 0.0001 response element (U22970, 1p36) TNF receptor-associated 5005.59 208.4321 4098.21 179.4619 0.0002 factor 1 (U19261, 9q33–q34) Adhesion molecule ninjurin 1 3715.181 268.8716 3157.01 122.5063 0.0003 (U91512, 9q22) TNF receptor-associated 2364.498 96.09024 2096.81 78.13522 0.0007 factor 1 (U19261, 9q33–q34) Transcription factor ISGF-3 1760.116 279.2687 1217.01 143.1016 0.014 (M97936, 2q32.2) interleukin-7 receptor 2027.312 184.3109 2307.75 174.5007 0.02 (AF043129, 5p13) Interleukin 8 (M28130, 13608.48 1609.251 17969.3 1448.031 0.02 4q13–q21) GRO2 oncogene (M35820, 6961.234 707.7638 5737.08 415.0885 0.001 4q21) Interferon-induced protein 1572.879 319.6902 713.604 178.921 0.009 with tetratricopeptide repeats 1 (M24594, 10q25–q26) interferon-induced protein 1759.121 304.6315 828.759 214.6707 0.005 with tetratricopeptide repeats 4 (AF026939, 10q24) Macrophage colony 3364.842 117.2874 4029.78 110.6731 0.04 stimulating factor 1 (m37435, 1p21–p13) Ras inhibitor (m37190, 20) 1611.356 177.1993 860.944 120.8235 0.0003 Small inducible cytokine A2 10048.81 755.9874 8072.22 495.8341 0.007 (M28225, 17q11.2–q21.1) Cyclooxygenase-2 3765.675 255.1504 2568.34 233.2174 <0.0001 U04636, 1q25.2–q25.3) Transforming growth factor, 740.8099 79.60563 844.935 86.39076 0.04 beta receptor II (70–80 kD) (D50683, 3p22) Cyclic AMP-responsive 6531.873 386.0808 5686.2 268.2961 0.017 element modulator beta isoform (S681634, 10p12.1–p11.1) Interleukin 1 receptor 13211.16 1126.502 9535.15 642.7608 <0.0001 antagonist (X52015, 2q14.2) Interleukin 6 (X04430, 7p21) 13482.49 852.9056 10099.2 625.5049 <0.0001 Nef-associated factor 1 5100.91 671.4467 4788.67 294.8366 0.004 (AJ011896, 5q32–q33.1) Small inducible cytokine A7 766.0895 165.4574 686.035 91.85867 0.02 (X72308, 17q11.2–q12) Small inducible cytokine A2 17108.01 1043.492 12566.7 742.7468 0.003 (M26683, 17q11.2–q21.1)

TABLE 5C L-1/TNF-INDUCED EXPRESSION VALUES WITH GC GC resistant GC sensitive (n = 30) (n = 66) Gene (Genbank, Map) MEAN SEM MEAN SEM P value Perforin 1 (M28393, 5932.6836 324.4973 4613.415 214.7653 0.0007 10q22) Tumor necrosis factor 7508.2896 451.0622 6416.635 365.9207 0.0731 receptor 2 (75 kD) (M32315, 1p36.3–p36.2) LPS-induced TNF-alpha 5786.222 306.7707 4946.478 171.3137 0.0127 factor (AF010312, 16p13.3–p12) Defensin (L12691, 8) 11441.772 1372.689 7818.661 922.0382 0.0251 KIAA0542 protein 5714.3643 1558.592 9333.106 1256.301 0.0201 (AB011114, 22q12.2) Beta 2-microglobulin 13301.895 2780.221 30811.06 3742.183 0.0027 (S82297, 15q21–q22.2) Granzyme B precursor 1877.4227 216.4729 1227.949 107.5656 0.003 (M57888, 14q11.2) Thymosin, beta 4 19120.795 1911.539 26315.6 1943.828 0.019 (M17733, Xq21.3–q22) HLA class I heavy chain 22299.861 2913.894 34067.19 3096.341 0.0145 (X58536, 6p21.3) Elongation factor EF-1- 18670.865 3364.731 33042.49 3553.801 0.0015 alpha (J04617, 6q14) EST (AI526078, 20q13.3) 17650.287 1587.158 25903.59 1771.242 0.0032 CD98 (M80244, 16q24.3) 5768.97 406.1578 4463.957 177.7024 0.0011 Nuclear factor kappa-B 6144.28 307.2589 5010.971 179.1457 0.0012 DNA binding subunit (M58603, 4q24) Signal transducer and 6800.764 284.0342 5744.643 184.6763 0.0021 activator of transcription 4 (L78440, 2q32.2–q32.3) Nef-associated factor 1 9655.332 495.7066 8075.491 259.7791 0.0026 (AJ011896, 5q32–q33.1) CD44 antigen (L05424, 4486.082 168.28 3818.656 133.3167 0.0042 11p13) (2′–5′)oligoadenylate 1667.9843 164.3503 1064.525 130.1923 0.0054 synthetase (AJ225089, 12q24.2) Interferon regulatory factor 2292.962 205.8358 1611.122 146.0624 0.007 7 isoform a (U53831, 11p15.5) Interferon-inducible 2946.7205 383.2516 1801.234 251.7923 0.0099 protein p78 (M33882, 21q22.3) interferon inducible 1-8U 8983.514 1053.469 6070.849 616.6636 0.0109 gene (X57352, 11) Interferon stimulated gene 6519.748 401.4738 5387.727 239.7823 0.012 (20 kD) (U88964, 15q26) Interferon-stimulated 6071.2886 915.0679 3757.143 548.3279 0.021 protein, 15 kDa (m13755, 1) CD44 isoform RC 3719.6047 178.2906 3236.746 115.0394 0.0225 (AF098641, 11p13) CD83 antigen (Z11697, 4931.093 295.5049 4274.713 230.1791 0.0354 6p23) Interferon stimulatable 2737.275 346.7752 1615.407 174.2281 0.011 response element (U22970, 1p36) TNF receptor-associated 3581.1409 185.2364 2935.34 116.3565 0.0027 factor 1 (U19621, 9q33–q34) Adhesion molecule 3173.9238 222.309 2405.162 110.9695 0.0008 ninjurin 1 (U01512, 9q22) TNF receptor-associated 1790.2013 89.11578 1506.127 60.22232 0.0085 factor 1 (U19261, 9q33–q34) Transcription factor ISGF-3 1490.314 178.5814 1048.989 102.7824 0.0212 (M97936, 2q32.2) interleukin-7 receptor 3344.87 271.804 4565.661 279.8599 0.0057 (AF043129, 5p13) Interleukin 8 12848.457 926.3713 14222.37 992.1271 ns** (M28130, 4q13–q21) GRO2 oncogene 5052.562 564.9373 4225.244 349.2652 ns** (M36820, 4q21) Interferon-induced protein 1094.8508 209.4019 672.1904 127.6253 ns** with tetratricopeptide repeats 1 (M24594, 10q25–q26) interferon-induced protein 1109.5538 162.5891 743.2253 123.5186 ns** with tetratricopeptide repeats 4 (AF026939, 10q24) Macrophage colony 3624.9211 172.8444 3983.53 132.856 ns** stimulating factor 1 (M37435, 1p21–p13) Ras inhibitor 1615.0812 183.4838 1285.366 118.4882 ns** (M37190, 20) Small inducible cytokine 3485.5967 409.9117 2908.787 418.8578 ns** A2 (M28225, 17q11.2–q21.2) Cyclooxygenase-2 1004.8341 151.2995 1012.272 148.0441 ns** (U04636, 1q25.2–q25.3) Transforming growth factor, 998.71014 94.93384 1135.562 93.18079 ns** beta receptor II (70–80 kD) (D50683, 3p22) Cyclic AMP-responsive 4027.2566 290.9312 4019.365 190.1472 ns** element modulator beta isoform (S68134, 10p12.2–p11.1) Interleukin 1 receptor 4454.89 509.2246 3887.395 543.3725 ns** antagonist (X52015, 2q14.2) Interleukin 6 (X04430, 6479.466 737.0165 5473.95 617.1881 ns** 7p21) Nef-associated factor 1 5710.245 619.1145 5202.576 322.5588 ns** (AJ011896, 5q32–q33.1) Small inducible cytokine 283.77304 23.31367 334.6954 61.4575 ns** A7 (X72308, 17q11.2–q12) Small inducible cytokine 5850.6055 630.0103 4954.649 646.567 ns** A2 (M26683, 17q11.2–q21.1)

TABLE 5D SET1/2 Genbank Systematic Common Description Map AL049963 40456_at Source: Homo sapiens 4 mRNA; cDNA DKFZp564A132 (from clone DKFZp564A132) S68134 32066_g_at CREM CREM-β 10p12.1–p11.1 M58603 1378_g_at KBF1 Human nuclear factor 4q24 kappa-B DNA binding subunit (NF-kβ) mRNA, complete cds M13755 1107_s_at ISG15 17-kDa protein 1 L31584 1097_s_at EBI1 BLR2 CMKBR7 Source: Human G-protein- 17q12–q21.2 coupled receptor (EBI 1) gene exon 3 AF022375 36100_at VEGFA H. sapiens vascular 6p12 endothelial growth factor mRNA, complete cds. M58603 1377_at KBF1 Human nuclear factor 4q24 kappa-B DNA binding subunit (NF-kβ) mRNA, complete cds M58603 38438_at KBF1 Human nuclear factor 4q24 kappa-B DNA binding subunit (NF-κβ) mRNA, complete cds U88964 33304_at HEM45 low level estrogen- 15q26 modulated; deduced ORF detected by anti-peptide antisera L08177 931_at EBI2 Epstein-Barr virus induced 13 gene 2 (lymphocyte-specific G protein-coupled receptor) D67031 33102_at ADDL Source: H. sapiens ADDL 10q24.2–q24.3 mRNA for adducin-like protein, complete cds. AL021977 36711_at HS5O6A DKFZP586A1024 C22orf5 22q12 V01512 1916_s_at c-fos Source: Human cellular 14q24.3 oncogene c-fos (complete sequence). X59417 36122_at IOTA PROS27 Source: H.sapiens PROS-27 14q13 mRNA HH BASELINE Rest ()*S X02910 1852_at TNFA DIF TNFSF2 Tumor necros factor 6p21.3 M15330 39402_at IL1B Source: Human interleukin 2q14 1-β (IL1B) mRNA, complete cds J04130 36674_at MIP-1-BETA SCYA4, MIP1B 17q21 M28130 1369_s_at IL8 Source: Human interleukin 4q13–q21 8 (IL8) gene, complete cds D90144 36103_at MIPA SCYA3 MIP1A 17q11–q21 S81914 1237_at IEX-1 radiation-inducible 6p21.3 immediate-early gene L11329 1292_at PAC-1 PAC1 PAC-1; putative 2q11 M57888 32370_at CSPB CTLA1 CCPI Source: Human (clone 14q11.2 lambda B34) CGL-1 CSP-B cytotoxic T-lymphocyte- associated serine esterase 1 (CTLA1) gene complete cds M16441 259_s_at TNFSF1 TNFB LT lymphotoxin 6p21.3 X68277 1005_at CL 100 Source: H. sapiens CL 100 5q34 mRNA for protein tyrosine phosphatase X78992 32588_s_at ERF2 TIS11D Source: H. sapiens ERF-2 2 mRNA L12691 31506_s_at DEF3 HNP-3 defensin 8 Z11697 37536_at CD83 a cell-surface molecule 6p23 expressed by interdigitating reticulum cells, Langerhans cells and activated lymphocytes. A member of immunoglobin superfamily M69199 38326_at G0S2 Source: Human G0S2 1q32.2–q41 protein gene, complete cds M17017 35372_r_at MDNCF SCYB8 IL8 IL8 4q13–q21 U09937 189_s_at URKR UPAR CD87 Source: Human urokinase- 19q13 type plasminogen receptor, exon 7 U27467 2002_s_at BCL2L5 BFL1 GRS HBPA1 Bcl-2 related; similar to 15q24.3 mouse hemopoietic-specific early-response protein, Swiss-Prot Accession Number Q07440; similar to mouse transforming protein Bcl-2-β, Swiss-Prot Accession Number P10418; Method: conceptual translation AL022315 37456_at dJ117715.1 supported by predicted 22q13.1 exons match: ESTs: Em:AA316883 M59465 595_at TNFA1P2 A20 tumor necrosis factor alpha 6 inducible protein U04636 1069_at hCox-2 Source: H. sapiens 1q25.2–q25.3 cyclooxygenase-2 (hCox-2) gene, complete cds

TABLE 5E Custom Genbank Common Product Keywords Field 2 AL049963 D4S1572 S68134 CREM cyclic AMP-responsive element D10S208 modulator beta isoform M58603 KBF1 nuclear factor kappa-B DNA nuclear factor kappa-B DNA D4S1572 binding subunit binding subunit M13755 ISG15 interferon-stimulated protein, 15 kDa interferon; interferon- D8S264 inducible protein L31584 EBI1 BLR2 chemokine (C—C motif) receptor 7 G protein-coupled receptor #N/A CMKBR7 AF022375 VEGFA vascular endothelial growth D6S282 factor M58603 KBF1 nuclear factor kappa-B DNA nuclear factor kappa-B DNA D4S1572 binding subunit binding subunit M58603 KBF1 nuclear factor of kappa light nuclear factor kappa-B DNA D4S1572 polypeptide gene enhancer in B- binding subunit cells 1 (p105) U88964 HEM45 interferon stimulated gene 20 kD #N/A L08177 EBI2 Epstein-Barr virus induced gene D13S1298 2 (lymphocyte-specific G protein-coupled receptor) D67031 ADDL adducin 3, isoform a adducin-like protein; ADDL D10S597 AL021977 HS5O6A chromosome 22 open reading HTG; CpG island; MAFF #N/A frame 5 DKFZP586A1024 V01512 c-fos v-fos FBJ murine osteosarcoma oncogene #N/A viral oncogene homolog X59417 IOTA prosomal P27K protein PROS-27 gene; prosomal #N/A PROS27 consensus; prosomal protein; RNA-binding protein X02910 TNFA DIF tumor necrosis factor (cachectin) signal peptide; tumor #N/A TNFSF2 necrosis factor M15330 IL1B interleukin 1β interleukin-1β D2S160 J04130 MIP-1-BETA small inducible cytokine A4 act2 gene; immune activation D17S933 (homologous to mouse Mip-1β) gene M28130 IL8 interleukin 8 interleukin 8 #N/A D90144 MIPA SCYA3 small inducible cytokine A3 LD78; LD78 alpha; cytokine; #N/A (homologous to mouse Mip-1α) inducible gene family; secreted peptide S81914 IEX-1 immediate early response 3 D6S1660 L11329 PAC-1 PAC1 protein tyrosine phosphatase protein-tyrosine phosphatase; D2S2264 tyrosine phosphatase M57888 CSPB CTLA1 granzyme B precursor cytotoxic T-lymphocyte- #N/A CCPI CGL-1 associated serine esterase 1 CSP-B M16441 TNFSF1 lymphotoxin alpha precursor lymphotoxin; tumor necrosis #N/A TNFB LT factor X68277 CL 100 protein-tyrosine phosphatase tyrosine phosphatase #N/A X78992 ERF2 TIS11D butyrate response factor 2 (EGF- ERF-2 gene D2S2259 response factor 2) L12691 DEF3 HNP-3 defensin, alpha 3, neutrophil- #N/A specific Z11697 CD83 CD83 antigen (activated B HB15 gene; immunoglobulin D6S259 lymphocytes, immunoglobulin superfamily superfamily) M69199 G0S2 putative lymphocyte G0/G1 G0S2 protein #N/A switch gene M17017 MDNCF interleukin 8 beta-thromboglobulin D4S3042 SCYB8 IL8 U09937 URKR UPAR plasminogen activator, urokinase #N/A CD87 receptor U27467 BCL2L5 BCL2-related protein A1 D15S1005 BFL1 GRS HBPA1 AL022315 dJ117715.1 lectin, galactoside-binding, HTG; CpG island; galectin; #N/A soluble, 2 (galectin 2) lectin LGALS2; MSE55 M59465 TNFA1P2 tumor necrosis factor, alpha- #N/A A20 induced protein 3 U04636 hCox-2 cyclooxygenase-2 #N/A

While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims. 

1. A method for diagnosing responders to a glucocorticoid drug for treating asthma from non-responders to the drug, comprising: a) obtaining a sample of cells from a human patient; b) optionally inducing a pro-inflammatory-like state by treating the sample with specific mediators; c) obtaining a gene expression profile from the sample, wherein the expression profile comprises expression values for one or more genes listed in Tables 1, 2A, 2B, 4A, 4B and 5A–5E; and d) comparing the gene expression profile of the sample with a reference gene expression profile indicative of responsiveness to the drug obtained from one or more patients who are responsive to the drug and/or a reference gene expression profile indicative of non-responsiveness to the drug obtained from one or more patients who are non-responsive to the drug, wherein similarity in expression profiles between the sample and reference profiles indicates glucocorticoid sensitivity in the patient from whom the sample was obtained, thereby diagnosing the patient as a responder or non-responder to the drug.
 2. The method of claim 1, wherein the expression profile comprises expression values for one or more genes listed in Tables 1, 2A and 2B.
 3. The method of claim 1, wherein the expression profile comprises expression values for one or more genes listed in Tables 4A and 4B.
 4. The method of claim 1, wherein the expression profile comprises expression values for one or more genes listed in Tables 5A–5E.
 5. The method of claim 1, further comprising exposing the sample to the drug for treating asthma prior to obtaining the gene expression profile of the sample.
 6. The method of claim 1, wherein the sample of cells is derived from peripheral blood mononuclear cells or neutrophils.
 7. The method of claim 1, wherein the sample is obtained via biopsy from synovial membrane or airway smooth muscle.
 8. The method of claim 1, wherein the gene expression profile of the sample is obtained using a hybridization assay to oligonucleotides contained in a microarray.
 9. The method of claim 8 comprising the use of hybridization probes that hybridize to polynucleotides corresponding to informative genes and reagents for detecting hybridization.
 10. The method of claim 1, wherein the reference expression profile is that of cells derived from patients who do not have asthma. 